Recent technological developments have resulted in a wealth of genetic sequence, genomic, expression, proteomic and other """"""""omic"""""""" data that will potentially transform our understanding of the architecture of complex traits and diseases. The complexity of the problem to use and understand all this data in human subjects, however, has resulted in the rate of development of statistical and methodological tools lagging behind the rate of influx of new data. The long term goal of this project is to develop analytical and computational methods that use these data to build statistical models that will lead to the pinpointing of individual variants, identification o networks of interacting genes, and understanding of how sets of measurable biological variables leads to realized quantitative and disease phenotypes. We focus our methodological development on studies of isolated populations, where for historic or demographic reasons the sampled population tends to be relatively genetically homogenous and where many of the individuals may be related, possibly cryptically. The relative lack of heterogeneity of isolated populations is advantageous for the discovery of genetic variants because genetic effects will tend to concentrate into fewer loci, and some of the variants that in the larger population are rare will be more frequent, aiding in their discovery. Furthermore, isolated populations often live in a more uniform environment, reducing this as a confounding effect. The unexpected correlations that arise, however, due to both recent and distant relatedness, provides both methodological challenges and the opportunity to use this information to obtain greater biological insight. In pursuit of the above stated goal, then, we will develop methods for use in isolated populations that use large genomic and other data sets that will lead to a greater understanding of the biological underpinnings of complex traits.
The specific aims of this proposal are (1) to develop methods for estimating identity by descent (IBD) using sequence data, (2) to develop methods for whole-genome complex trait mapping using IBD, (3) to develop methods for whole-genome association mapping using all observed variants simultaneously, and (4) to develop methods using multiple omics data sets to create comprehensive models for complex traits. In addition to developing the statistical methods we will implement them in computationally efficient open-source software packages. These packages will be promptly made available to the wider genetic analysis community.

Public Health Relevance

Recent technological advances have led to a large influx of genomic and related biological data sets becoming obtainable by researchers. The goal is to use these data to both better understand the underlying biology of complex traits and diseases and to deliver more effective, personalized medical care. Accomplishing this, however, requires analytical tools and software. Our goal is to develop these tools and make them freely available to researchers and biomedical personnel.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Research Project (R01)
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Genomics, Computational Biology and Technology Study Section (GCAT)
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Brooks, Lisa
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University of Chicago
Schools of Medicine
United States
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Papachristou, Charalampos; Ober, Carole; Abney, Mark (2016) A LASSO penalized regression approach for genome-wide association analyses using related individuals: application to the Genetic Analysis Workshop 19 simulated data. BMC Proc 10:221-226
Livne, Oren E; Han, Lide; Alkorta-Aranburu, Gorka et al. (2015) PRIMAL: Fast and accurate pedigree-based imputation from sequence data in a founder population. PLoS Comput Biol 11:e1004139
Abney, Mark (2015) Permutation testing in the presence of polygenic variation. Genet Epidemiol 39:249-58
Joshi, Peter K (see original citation for additional authors) (2015) Directional dominance on stature and cognition in diverse human populations. Nature 523:459-462
Campbell, Catarina D; Mohajeri, Kiana; Malig, Maika et al. (2014) Whole-genome sequencing of individuals from a founder population identifies candidate genes for asthma. PLoS One 9:e104396
Yao, Tsung-Chieh; Du, Gaixin; Han, Lide et al. (2014) Genome-wide association study of lung function phenotypes in a founder population. J Allergy Clin Immunol 133:248-55.e1-10
Parker, Clarissa C; Carbonetto, Peter; Sokoloff, Greta et al. (2014) High-resolution genetic mapping of complex traits from a combined analysis of F2 and advanced intercross mice. Genetics 198:103-16
Cheng, Riyan; Parker, Clarissa C; Abney, Mark et al. (2013) Practical considerations regarding the use of genotype and pedigree data to model relatedness in the context of genome-wide association studies. G3 (Bethesda) 3:1861-7
Han, Lide; Abney, Mark (2013) Using identity by descent estimation with dense genotype data to detect positive selection. Eur J Hum Genet 21:205-11
Stanhope, Stephen A; Abney, Mark (2012) GLOGS: a fast and powerful method for GWAS of binary traits with risk covariates in related populations. Bioinformatics 28:1553-4

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